We present an all-around study of the visitors flow in crowded museums: a combination of Lagrangian field measurements and statistical analyses enable us to create stochastic digital-twins of the guest dynamics, unlocking comfort- and safety-driven optimizations. Our case study is the Galleria Borghese museum in Rome (Italy), in which we performed a real-life data acquisition campaign.

We specifically employ a Lagrangian IoT-based visitor tracking system based on Raspberry Pi receivers, displaced in fixed positions throughout the museum rooms, and on portable Bluetooth Low Energy beacons handed over to the visitors. Thanks to two algorithms: a sliding window-based statistical analysis and an MLP neural network, we filter the beacons RSSI and accurately reconstruct visitor trajectories at room-scale. Via a clustering analysis, hinged on an original Wasserstein-like trajectory-space metric, we analyze the visitors paths to get behavioral insights, including the most common flow patterns. On these bases, we build the transition matrix describing, in probability, the room-scale visitor flows. Such a matrix is the cornerstone of a stochastic model capable of generating visitor trajectories in silico. We conclude by employing the simulator to enhance the museum fruition while respecting numerous logistic and safety constraints. This is possible thanks to optimized ticketing and new entrance/exit management.

我们对拥挤的博物馆中的游客流量进行了全面研究：拉格朗日实地测量和统计分析的结合使我们能够创建来宾动态的随机数字双胞胎，从而释放舒适性和安全性驱动的优化。我们的案例研究是位于罗马（意大利）的博尔盖塞美术馆（Galleria Borghese），我们在其中进行了真实的数据采集活动。

我们特别采用了基于拉格朗日基于IoT的访客跟踪系统，该系统基于Raspberry Pi接收器，在整个博物馆房间内固定放置，并使用移交给参观者的便携式蓝牙低功耗信标。得益于两种算法：基于滑动窗口的统计分析和MLP神经网络，我们对信标RSSI进行了过滤，并在房间范围内准确地重建了访客轨迹。通过基于原始的类似Wasserstein的轨迹空间度量的聚类分析，我们分析访问者的路径以获得行为见解，包括最常见的流模式。在这些基础上，我们建立了转移矩阵，该转移矩阵以概率描述了房间规模的访客流量。这样的矩阵是能够在计算机上生成访客轨迹的随机模型的基石。最后，我们通过使用模拟器来增强博物馆的成就，同时尊重众多的物流和安全约束。这要归功于优化的票务和新的进/出管理。